---
title: "Coronavirus in India Dashboard"
author: "by Mithun Ghosh"
output:
flexdashboard::flex_dashboard:
orientation: rows
# social: ["facebook", "twitter", "linkedin"]
source_code: embed
vertical_layout: fill
---
```{r setup, include=FALSE}
#------------------ Packages ------------------
library(flexdashboard)
# install.packages("devtools")
# devtools::install_github("RamiKrispin/coronavirus", force = TRUE)
#library(coronavirus)
#data(coronavirus)
#update_datasets()
# View(coronavirus)
# max(coronavirus$date)
coronavirus <- read.csv(file="https://raw.githubusercontent.com/RamiKrispin/coronavirus-csv/master/coronavirus_dataset.csv",header=TRUE,sep=",")
`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "purple"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "red"
#------------------ Data ------------------
df <- coronavirus %>%
# dplyr::filter(date == max(date)) %>%
dplyr::filter(Country.Region == "India") %>%
dplyr::group_by(Country.Region, type) %>%
dplyr::summarise(total = sum(cases)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
# dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death) -ifelse(is.na(recovered), 0, recovered) ) %>%
dplyr::arrange(-confirmed) %>%
dplyr::ungroup() %>%
dplyr::mutate(country = dplyr::if_else(Country.Region == "United Arab Emirates", "UAE", as.character(Country.Region))) %>%
dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
dplyr::mutate(country = trimws(country)) %>%
dplyr::mutate(country = factor(country, levels = country))
df_daily <- coronavirus %>%
dplyr::filter(Country.Region == "India") %>%
dplyr::group_by(date, type) %>%
dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
dplyr::arrange(date) %>%
dplyr::ungroup() %>%
dplyr::mutate(active = confirmed - death -recovered) %>%
dplyr::mutate(
confirmed_cum = cumsum(confirmed),
death_cum = cumsum(death),
recovered_cum = cumsum(recovered),
active_cum = cumsum(active)
)
df1 <- coronavirus %>% dplyr::filter(date == max(as.character(date)))
```
Summary for India
=======================================================================
Row {data-width=400}
-----------------------------------------------------------------------
### confirmed {.value-box}
```{r}
valueBox(
value = paste(format(sum(df$confirmed), big.mark = ","), "", sep = " "),
caption = "Total confirmed cases",
icon = "fas fa-user-md",
color = confirmed_color
)
```
### active {.value-box}
```{r}
valueBox(
value = paste(format(sum(df$unrecovered, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(df$unrecovered, na.rm = TRUE) / sum(df$confirmed), 1),
"%)",
sep = ""
),
caption = "Active cases (% of total cases)", icon = "fas fa-ambulance",
color = active_color
)
```
### death {.value-box}
```{r}
valueBox(
value = paste(format(sum(df$death, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(df$death, na.rm = TRUE) / sum(df$confirmed), 1),
"%)",
sep = ""
),
caption = "Death cases (death rate)",
icon = "fas fa-heart-broken",
color = death_color
)
```
Row
-----------------------------------------------------------------------
### **Daily cumulative cases by type** (India only)
```{r}
df_daily <- df_daily %>% dplyr::filter(as.character(date) > "2020-01-31")
plotly::plot_ly(data = df_daily) %>%
plotly::add_trace(
x = ~date,
# y = ~active_cum,
y = ~confirmed_cum,
type = "scatter",
mode = "lines+markers",
# name = "Active",
name = "Confirmed",
line = list(color = confirmed_color),
marker = list(color = confirmed_color)
) %>%
plotly::add_trace(
x = ~date,
y = ~ confirmed_cum - recovered_cum - death_cum,
type = "scatter",
mode = "lines+markers",
name = "Active Cases",
line = list(color = active_color),
marker = list(color = active_color)
) %>%
plotly::add_trace(
x = ~date,
y = ~ recovered_cum,
type = "scatter",
mode = "lines+markers",
name = "Recovered",
line = list(color = recovered_color),
marker = list(color = recovered_color)
) %>%
plotly::add_trace(
x = ~date,
y = ~death_cum,
type = "scatter",
mode = "lines+markers",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color)
) %>%
plotly::add_annotations(
x = as.Date("2020-01-31"),
y = 1,
text = paste("First case"),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = -10,
ay = -90
) %>%
plotly::add_annotations(
x = as.Date("2020-03-12"),
y = 3,
text = paste("First death"),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = -90,
ay = -90
) %>%
plotly::add_annotations(
x = as.Date("2020-03-22"),
y = 14,
text = paste(
"New containment",
"
",
"measures"
),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = -10,
ay = -90
) %>%
plotly::layout(
title = "",
yaxis = list(title = "Cumulative number of cases"),
xaxis = list(title = "Date"),
legend = list(x = 0.1, y = 0.9),
hovermode = "compare"
)
```
Row
-----------------------------------------------------------------------
### **Daily cumulative cases by type Log Scale** (India only)
```{r}
plotly::plot_ly(data = df_daily) %>%
plotly::add_trace(
x = ~date,
# y = ~active_cum,
y = ~log(confirmed_cum),
type = "scatter",
mode = "lines+markers",
# name = "Active",
name = "Confirmed",
line = list(color = confirmed_color),
marker = list(color = confirmed_color)
) %>%
plotly::add_trace(
x = ~date,
y = ~ log(confirmed_cum - recovered_cum - death_cum),
type = "scatter",
mode = "lines+markers",
name = "Active Cases",
line = list(color = active_color),
marker = list(color = active_color)
) %>%
plotly::add_trace(
x = ~date,
y = ~ log(recovered_cum),
type = "scatter",
mode = "lines+markers",
name = "Recovered",
line = list(color = recovered_color),
marker = list(color = recovered_color)
) %>%
plotly::add_trace(
x = ~date,
y = ~log(death_cum),
type = "scatter",
mode = "lines+markers",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color)
) %>%
plotly::layout(
title = "",
yaxis = list(title = "Cumulative number of cases"),
xaxis = list(title = "Date"),
legend = list(x = 0.1, y = 0.9),
hovermode = "compare"
)
```
Comparison
=========================================================================================
### ** Daily Growth Rate of Total Cases from March (India Only)**
```{r}
df_april <- df_daily %>% dplyr::filter(as.character(date) > "2020-03-06") %>% dplyr::mutate(total_case_growth = confirmed_cum / dplyr::lag(confirmed_cum,k=1)) %>%
dplyr::mutate(active_case_growth = confirmed_cum / dplyr::lag(active_cum,k=1)) %>%
dplyr::mutate(death_case_growth = confirmed_cum / dplyr::lag(death_cum,k=1))
plotly::plot_ly(data = df_april) %>%
plotly::add_trace(
x = ~date,
y = ~ total_case_growth - 1,
type = "scatter",
mode = "lines+markers",
name = " Daily Growth Rate of Total Cases",
line = list(color = active_color),
marker = list(color = active_color)
) %>%
plotly::add_trace(
x = ~date,
y = ~ active_case_growth - 1,
type = "scatter",
mode = "lines+markers",
name = " Daily Growth Rate of Active Cases",
line = list(color = recovered_color),
marker = list(color = recovered_color)
) %>%
# plotly::add_trace(
# x = ~date,
# y = ~ death_case_growth - 1,
# type = "scatter",
# mode = "lines+markers",
# name = " Daily Growth Rate of Death Cases",
# line = list(color = active_color),
# marker = list(color = death_color)
# ) %>%
plotly::layout(
title = "",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Daily Growth Rate"),
xaxis = list(title = "Date")
)
```
-------------------------------------------------------------------------------------
### **Daily new confirmed cases from March**
```{r}
daily_confirmed <- coronavirus %>%
dplyr::filter(type == "confirmed") %>%
dplyr::filter(as.Date(date) >= "2020-03-06") %>%
dplyr::mutate(country = Country.Region) %>%
dplyr::group_by(date, country) %>%
dplyr::summarise(total = sum(cases)) %>%
dplyr::ungroup() %>%
tidyr::pivot_wider(names_from = country, values_from = total)
#----------------------------------------
# Plotting the data
daily_confirmed %>%
plotly::plot_ly() %>%
plotly::add_trace(
x = ~date,
y = ~India,
type = "scatter",
mode = "lines+markers",
name = "India"
) %>%
plotly::add_trace(
x = ~date,
y = ~France,
type = "scatter",
mode = "lines+markers",
name = "France"
) %>%
plotly::add_trace(
x = ~date,
y = ~Spain,
type = "scatter",
mode = "lines+markers",
name = "Spain"
) %>%
plotly::add_trace(
x = ~date,
y = ~Italy,
type = "scatter",
mode = "lines+markers",
name = "Italy"
) %>%
plotly::add_trace(
x = ~date,
y = ~US,
type = "scatter",
mode = "lines+markers",
name = "US"
) %>%
plotly::add_trace(
x = ~date,
y = ~Germany,
type = "scatter",
mode = "lines+markers",
name = "Germany"
) %>%
plotly::layout(
title = "",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Number of new confirmed cases"),
xaxis = list(title = "Date"),
hovermode = "compare",
margin = list(
b = 10,
t = 10,
pad = 2
)
)
```
Country Wise
========================================================================================
### **Cases distribution by Country**
```{r daily_summary}
df_EU <- coronavirus %>%
# dplyr::filter(date == max(date)) %>%
dplyr::filter(Country.Region %in% c("US","Spain","Italy","France","Germany","China","Iran","United Kingdom","Turkey","Belgium","Canada","Brazil","Netherlands","Russia",
"Switzerland","Portugal","Austria","Ireland","Israel","India")) %>%
dplyr::group_by(Country.Region, type) %>%
dplyr::summarise(total = sum(cases)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
# dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
dplyr::arrange(confirmed) %>%
dplyr::ungroup() %>%
dplyr::mutate(country = dplyr::if_else(Country.Region == "United Arab Emirates", "UAE", as.character(Country.Region))) %>%
dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
dplyr::mutate(country = trimws(country)) %>%
dplyr::mutate(country = factor(country, levels = country))
plotly::plot_ly(
data = df_EU,
x = ~country,
# y = ~unrecovered,
y = ~ confirmed - recovered -death,
# text = ~ confirmed,
# textposition = 'auto',
type = "bar",
name = "Active",
marker = list(color = active_color)
) %>%
plotly::add_trace(
y = ~death,
# text = ~ death,
# textposition = 'auto',
name = "Death",
marker = list(color = death_color)
) %>%
plotly::add_trace(
y = ~recovered,
name = "Recovered",
marker = list(color = recovered_color)
)%>%
plotly::layout(
barmode = "stack",
yaxis = list(title = "Total cases"),
xaxis = list(title = ""),
hovermode = "compare",
legend = list(x = 0.1, y = 0.9),
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
)
)
```
Map
=======================================================================
### **World map of cases** (*use + and - icons to zoom in/out*)
```{r}
# map tab added by Art Steinmetz
library(leaflet)
#library(leafpop)
library(purrr)
cv_data_for_plot <- coronavirus %>%
# dplyr::filter(Country.Region == "India") %>%
dplyr::filter(cases > 0) %>%
dplyr::group_by(Country.Region, Province.State, Lat, Long, type) %>%
dplyr::summarise(cases = sum(cases)) %>%
dplyr::mutate(log_cases = 2 * log(cases)) %>%
dplyr::ungroup()
cv_data_for_plot.split <- cv_data_for_plot %>% split(cv_data_for_plot$type)
pal <- colorFactor(c("orange", "red", "green"), domain = c("confirmed", "death", "recovered"))
map_object <- leaflet() %>% addProviderTiles(providers$Stamen.Toner)
names(cv_data_for_plot.split) %>%
purrr::walk(function(df) {
map_object <<- map_object %>%
addCircleMarkers(
data = cv_data_for_plot.split[[df]],
lng = ~Long, lat = ~Lat,
# label=~as.character(cases),
color = ~ pal(type),
stroke = FALSE,
fillOpacity = 0.8,
radius = ~log_cases,
popup = leafpop::popupTable(cv_data_for_plot.split[[df]],
feature.id = FALSE,
row.numbers = FALSE,
zcol = c("type", "cases", "Country.Region", "Province.State")
),
group = df,
# clusterOptions = markerClusterOptions(removeOutsideVisibleBounds = F),
labelOptions = labelOptions(
noHide = F,
direction = "auto"
)
)
})
map_object %>%
addLayersControl(
overlayGroups = names(cv_data_for_plot.split),
options = layersControlOptions(collapsed = FALSE)
)
```
Summary for US
=======================================================================
```{r}
df_US <- coronavirus %>%
# dplyr::filter(date == max(date)) %>%
dplyr::filter(Country.Region == "US") %>%
dplyr::group_by(Country.Region, type) %>%
dplyr::summarise(total = sum(cases)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
# dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death) - ifelse(is.na(recovered), 0, recovered)) %>%
dplyr::arrange(-confirmed) %>%
dplyr::ungroup() %>%
dplyr::mutate(country = dplyr::if_else(Country.Region == "United Arab Emirates", "UAE", as.character(Country.Region))) %>%
dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
dplyr::mutate(country = trimws(country)) %>%
dplyr::mutate(country = factor(country, levels = country))
df_daily_US <- coronavirus %>%
dplyr::filter(Country.Region == "US") %>%
dplyr::group_by(date, type) %>%
dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
dplyr::arrange(date) %>%
dplyr::ungroup() %>%
dplyr::mutate(active = confirmed - death -recovered) %>%
dplyr::mutate(
confirmed_cum = cumsum(confirmed),
death_cum = cumsum(death),
recovered_cum = cumsum(recovered),
active_cum = cumsum(active)
)
```
Row {data-width=400}
-----------------------------------------------------------------------
### confirmed {.value-box}
```{r}
valueBox(
value = paste(format(sum(df_US$confirmed), big.mark = ","), "", sep = " "),
caption = "Total confirmed cases",
icon = "fas fa-user-md",
color = confirmed_color
)
```
### active {.value-box}
```{r}
valueBox(
value = paste(format(sum(df_US$unrecovered, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(df_US$unrecovered, na.rm = TRUE) / sum(df_US$confirmed), 1),
"%)",
sep = ""
),
caption = "Active cases (% of total cases)", icon = "fas fa-ambulance",
color = active_color
)
```
### death {.value-box}
```{r}
valueBox(
value = paste(format(sum(df_US$death, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(df_US$death, na.rm = TRUE) / sum(df_US$confirmed), 1),
"%)",
sep = ""
),
caption = "Death cases (death rate)",
icon = "fas fa-heart-broken",
color = death_color
)
```
Row
-----------------------------------------------------------------------
### **Daily cumulative cases by type** (United States only)
```{r}
plotly::plot_ly(data = df_daily_US) %>%
plotly::add_trace(
x = ~date,
# y = ~active_cum,
y = ~confirmed_cum,
type = "scatter",
mode = "lines+markers",
# name = "Active",
name = "Confirmed",
line = list(color = confirmed_color),
marker = list(color = confirmed_color)
) %>%
plotly::add_trace(
x = ~date,
y = ~ confirmed_cum - recovered_cum - death_cum,
type = "scatter",
mode = "lines+markers",
name = "Active Cases",
line = list(color = active_color),
marker = list(color = active_color)
) %>%
plotly::add_trace(
x = ~date,
y = ~ recovered_cum,
type = "scatter",
mode = "lines+markers",
name = "Recovered",
line = list(color = recovered_color),
marker = list(color = recovered_color)
) %>%
plotly::add_trace(
x = ~date,
y = ~death_cum,
type = "scatter",
mode = "lines+markers",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color)
) %>%
plotly::add_annotations(
x = as.Date("2020-01-22"),
y = 1,
text = paste("First case"),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = -10,
ay = -90
) %>%
plotly::add_annotations(
x = as.Date("2020-02-29"),
y = 3,
text = paste("First death"),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = -90,
ay = -90
) %>%
plotly::layout(
title = "",
yaxis = list(title = "Cumulative number of cases"),
xaxis = list(title = "Date"),
legend = list(x = 0.1, y = 0.9),
hovermode = "compare"
)
```
Row
-----------------------------------------------------------------------
### **Daily cumulative cases by type Log Scale** (United States only)
```{r}
plotly::plot_ly(data = df_daily_US) %>%
plotly::add_trace(
x = ~date,
# y = ~active_cum,
y = ~log(confirmed_cum),
type = "scatter",
mode = "lines+markers",
# name = "Active",
name = "Confirmed",
line = list(color = confirmed_color),
marker = list(color = confirmed_color)
) %>%
plotly::add_trace(
x = ~date,
y = ~ log(confirmed_cum - recovered_cum - death_cum),
type = "scatter",
mode = "lines+markers",
name = "Active Cases",
line = list(color = active_color),
marker = list(color = active_color)
) %>%
plotly::add_trace(
x = ~date,
y = ~ log(recovered_cum),
type = "scatter",
mode = "lines+markers",
name = "Recovered",
line = list(color = recovered_color),
marker = list(color = recovered_color)
) %>%
plotly::add_trace(
x = ~date,
y = ~log(death_cum),
type = "scatter",
mode = "lines+markers",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color)
) %>%
plotly::layout(
title = "",
yaxis = list(title = "Cumulative number of cases"),
xaxis = list(title = "Date"),
legend = list(x = 0.1, y = 0.9),
hovermode = "compare"
)
```